• library(ggplot2)
• ggplot(data, aes(x, y, group, ...)) + geom_obj(...) + modify()
• or a quick and dirty alternative: qplot
• Usage:
  • data: an object of class data.frame
    • DATA of your plot
  • aes: a function that returns aesthetic mappings
    • VARIABLE in data to be plotted
  • geom_obj: geometric objects
    • TYPE of plot
    • geom_bar(), geom_line(), geom_point(), …
  • modify: additional modification to the plot
    • ggtitle(), scale_x_discrete(), theme_minimal()…

ggplot(data=diamonds, aes(x=cut)) + geom_bar()

• Variable class affects ggplot's behavior
• Variable class affects ggplot's behavior. Twice.
• Always check your data.frame (use str or class) before calling ggplot

str(diamonds)

## 'data.frame':    53940 obs. of  10 variables:
##  $ carat  : num  0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ...
##  $ cut    : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ...
##  $ color  : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6 7 7 6 5 2 5 ...
##  $ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ...
##  $ depth  : num  61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ...
##  $ table  : num  55 61 65 58 58 57 57 55 61 61 ...
##  $ price  : int  326 326 327 334 335 336 336 337 337 338 ...
##  $ x      : num  3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ...
##  $ y      : num  3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ...
##  $ z      : num  2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ...

head(diamonds)

##   carat       cut color clarity depth table price    x    y    z
## 1  0.23     Ideal     E     SI2  61.5    55   326 3.95 3.98 2.43
## 2  0.21   Premium     E     SI1  59.8    61   326 3.89 3.84 2.31
## 3  0.23      Good     E     VS1  56.9    65   327 4.05 4.07 2.31
## 4  0.29   Premium     I     VS2  62.4    58   334 4.20 4.23 2.63
## 5  0.31      Good     J     SI2  63.3    58   335 4.34 4.35 2.75
## 6  0.24 Very Good     J    VVS2  62.8    57   336 3.94 3.96 2.48

ggplot(data=diamonds, aes(x=cut)) + geom_bar()

ggp <- ggplot(data=diamonds[1:5,], aes(x=cut)) + geom_bar()
ggp

ggp <- ggplot(data=diamonds[1:5,], aes(x=cut)) + geom_bar()
ggp + scale_x_discrete(drop=FALSE) # ?scale_x_discrete

ggplot(data=diamonds, aes(x=cut)) + geom_bar() + coord_flip()

ggp <- ggp + xlab("Cut") + ylab("Count") + ggtitle("Hello ggplot!")
ggp

# want to customize colors? refer to: www.cookbook-r.com/Graphs/Colors_(ggplot2)/
ggp + geom_bar(fill="snow", color="black") # see colors() if you're picky

diamonds_precounted <- as.data.frame(table(diamonds$cut, dnn=c("Cut")))
diamonds_precounted

##         Cut  Freq
## 1      Fair  1610
## 2      Good  4906
## 3 Very Good 12082
## 4   Premium 13791
## 5     Ideal 21551

• take a guess before you try!
• ggplot(data=diamonds_precounted, aes(x=Cut)) + geom_bar() # epic FAIL

ggplot(data=diamonds_precounted, aes(x=Cut)) + geom_bar()

ggplot(diamonds_precounted, aes(x=Cut, y=Freq)) + geom_bar(stat="identity") # default is "bin"

• row should be unique
  • otherwise counts will be summed up
• missing label will be present at default
  • differ from stat="bin"
• negative bar is allowed

diamonds[1:5,]

##   carat     cut color clarity depth table price    x    y    z
## 1  0.23   Ideal     E     SI2  61.5    55   326 3.95 3.98 2.43
## 2  0.21 Premium     E     SI1  59.8    61   326 3.89 3.84 2.31
## 3  0.23    Good     E     VS1  56.9    65   327 4.05 4.07 2.31
## 4  0.29 Premium     I     VS2  62.4    58   334 4.20 4.23 2.63
## 5  0.31    Good     J     SI2  63.3    58   335 4.34 4.35 2.75

ggplot(diamonds[1:5,], aes(x=cut, y=depth)) + geom_bar(stat="identity")

ggplot(diamonds_precounted, aes(x=reorder(Cut, -Freq), y=Freq)) + 
  geom_bar(stat='identity') # The order is determined by factor levels

ggplot(data=diamonds, aes(x=color, fill=cut)) + geom_bar()

ggplot(data=diamonds, aes(x=color, color=cut)) + geom_bar()

ggplot(data=diamonds, aes(x=color, fill=cut)) + geom_bar(position="dodge")

ggplot(data=diamonds, aes(x=price)) + geom_bar()

ggplot(data=diamonds, aes(x=price, fill=cut)) + geom_bar(position="stack")

ggplot(data=diamonds, aes(x=price, fill=cut)) + geom_bar(position="identity", alpha=.5)

ggplot(data=diamonds, aes(x=price, fill=cut)) + geom_density(position="identity", alpha=.5)

ggplot(data=diamonds[diamonds$cut %in% c("Fair", "Ideal"),], aes(x=price, fill=cut)) + 
    geom_density(position="identity", alpha=.5) + 
    geom_bar(position="identity", alpha=.5)

ggplot(data=diamonds[diamonds$cut %in% c("Fair", "Ideal"),], aes(x=price, y=..density.., fill=cut)) + 
    geom_density(position="identity", alpha=.5) + 
    geom_bar(position="identity", alpha=.5)

• use the weather data to plot:
  • total rainy hours for each city (PrecpHour)
  • total rainy volume for each city (Precp)
  • total sunny hours for each city (SunShine)
  • total hours grouped by rainy / sunny for each city
• remember to check variable type in advanced
• the forth plot is rather difficult

library(ggplot2)
source("./R/load-all.R")
dat[dat$Precp == 'T', "Precp"] <- '0'
dat$Precp <- as.numeric(dat$Precp)

ggplot(dat, aes(x=city, y=PrecpHour)) + geom_bar(stat="identity")
ggplot(dat, aes(x=city, y=Precp)) + geom_bar(stat="identity")
ggplot(dat, aes(x=city, y=SunShine)) + geom_bar(stat="identity")

dd <- by(dat, dat$city, 
         function(x) c(rainyHour=sum(x$PrecpHour), sunnyHour=sum(x$SunShine)))
dd <- as.data.frame(do.call(rbind, dd))
dd$city <- rownames(dd)
dd <- reshape(dd, direction="long", varying=c("rainyHour", "sunnyHour"), 
              idvar="city", v.names="hour", times=c("rainyHour", "sunnyHour"), timevar="status")
ggplot(dd, aes(x=city, y=hour, fill=status)) + geom_bar(stat="identity")

library(ggplot2)
library(data.table) # need 1.9.5 => devtools:install_github("Rdatatable/data.table", build_vignettes=FALSE)
src_path <- "./data"
citynames <- unique(gsub("_.*", '', dir(src_path)))
citynames <- citynames[1:4] # take english version only
dat <- list()
for ( city in citynames )
    dat[[city]] <- rbindlist(lapply(grep(city, dir(src_path, full.names=TRUE), value=TRUE), fread))
dat <- rbindlist(dat, idcol="city")
dat[Precp == 'T', Precp:='0']
dat[, Precp:=as.numeric(Precp)]

for ( var in c("PrecpHour", "Precp", "SunShine") ) 
    print(ggplot(dat, aes_string(x="city", y=var)) + geom_bar(stat="identity"))

dd <- melt(dat[, list(PrecpHour=sum(PrecpHour), SunShine=sum(SunShine)), by="city"], 
           id.vars="city", measure.vars=c("PrecpHour", "SunShine"), 
           variable.name="status", value.name="hour")
ggplot(dd, aes(x=city, y=hour, fill=status)) + geom_bar(stat="identity")

##     color       cut  cnt        pct
##  1:     E     Ideal 3903 0.39838726
##  2:     E   Premium 2337 0.23854241
##  3:     E      Good  933 0.09523323
##  4:     I   Premium 1428 0.26337145
##  5:     J      Good  307 0.10933048
##  6:     J Very Good  678 0.24145299
##  7:     I Very Good 1204 0.22205828
##  8:     H Very Good 1824 0.21965318
##  9:     E      Fair  224 0.02286414
## 10:     J     Ideal  896 0.31908832
## 11:     F   Premium 2331 0.24428841
## 12:     I     Ideal 2093 0.38601992
## 13:     I      Good  522 0.09627444
## 14:     E Very Good 2400 0.24497295
## 15:     G Very Good 2299 0.20359547
## 16:     D Very Good 1513 0.22332103
## 17:     F Very Good 2164 0.22678684
## 18:     F      Good  909 0.09526305
## 19:     H      Good  702 0.08453757
## 20:     D      Good  662 0.09771218

diamonds$cnt <- 0L
diamondsDF <- aggregate(data=diamonds, cnt ~ color + cut, FUN=length)
diamondsDF <- do.call(rbind, lapply(split(diamondsDF, diamondsDF$color), 
                                  function(x) within(x, pct <- cnt / sum(cnt))))
head(diamondsDF)

##      color       cut  cnt        pct
## D.1      D      Fair  163 0.02405904
## D.8      D      Good  662 0.09771218
## D.15     D Very Good 1513 0.22332103
## D.22     D   Premium 1603 0.23660517
## D.29     D     Ideal 2834 0.41830258
## E.2      E      Fair  224 0.02286414

DF <- as.data.frame(table(diamonds$color, diamonds$cut, dnn=list("color", "cut")))
DF <- do.call(rbind, by(DF, DF$color, 
                        function(x) within(x, pct <- Freq / sum(Freq))))
head(DF)

##      color       cut Freq        pct
## D.1      D      Fair  163 0.02405904
## D.8      D      Good  662 0.09771218
## D.15     D Very Good 1513 0.22332103
## D.22     D   Premium 1603 0.23660517
## D.29     D     Ideal 2834 0.41830258
## E.2      E      Fair  224 0.02286414

diamonds$cnt <- ave(rep(1, nrow(diamonds)), diamonds$color, diamonds$cut, FUN=sum)
diamonds$cnt_by_color <- ave(rep(1, nrow(diamonds)), diamonds$color, FUN=sum)
diamonds <- transform(diamonds, pct=cnt / cnt_by_color)
DFave <- unique(diamonds[,c("color", "cut", "cnt", "pct")])
head(DFave)

##   color       cut  cnt        pct
## 1     E     Ideal 3903 0.39838726
## 2     E   Premium 2337 0.23854241
## 3     E      Good  933 0.09523323
## 4     I   Premium 1428 0.26337145
## 5     J      Good  307 0.10933048
## 6     J Very Good  678 0.24145299

library(data.table)
diamondsDT <- as.data.table(diamonds)
diamondsDT <- diamondsDT[, list(cnt=.N), by="color,cut"]
diamondsDT <- diamondsDT[, pct:=cnt / sum(cnt), by="color"]
head(diamondsDT)

##    color       cut  cnt        pct
## 1:     E     Ideal 3903 0.39838726
## 2:     E   Premium 2337 0.23854241
## 3:     E      Good  933 0.09523323
## 4:     I   Premium 1428 0.26337145
## 5:     J      Good  307 0.10933048
## 6:     J Very Good  678 0.24145299

library(dplyr)
DP <- summarise(group_by(diamonds, color, cut), cnt=n())
DP <- mutate(DP, pct=cnt / sum(cnt))
head(DP)

## Source: local data frame [6 x 4]
## Groups: color
## 
##   color       cut  cnt        pct
## 1     D      Fair  163 0.02405904
## 2     D      Good  662 0.09771218
## 3     D Very Good 1513 0.22332103
## 4     D   Premium 1603 0.23660517
## 5     D     Ideal 2834 0.41830258
## 6     E      Fair  224 0.02286414

# options(gsubfn.engine='R') # try this line should you have trouble loading the package
library(sqldf)
tmp1 <- sqldf('select color, cut, count(*) as cnt from diamonds group by color, cut')
tmp2 <- sqldf('select color, sum(cnt) as cnt_by_color from tmp1 group by color')
DFSQL <- sqldf('select tmp1.color, cut, cnt, (cnt*1.0 / cnt_by_color) as pct 
               from tmp1 join tmp2 on tmp1.color = tmp2.color')
head(DFSQL)

##   color       cut  cnt        pct
## 1     D      Fair  163 0.02405904
## 2     D      Good  662 0.09771218
## 3     D     Ideal 2834 0.41830258
## 4     D   Premium 1603 0.23660517
## 5     D Very Good 1513 0.22332103
## 6     E      Fair  224 0.02286414

library(ggplot2)
dd2 <- do.call(rbind, by(dd, dd$city, 
                        function(x) within(x, pct <- hour / sum(hour))))
ggplot(dd2, aes(x=city, y=pct, fill=status)) + geom_bar(stat="identity")

# not meaningful but plottable
ggplot(pressure, aes(x=temperature, y=pressure)) + geom_line(size=1.5) 

• Mnay of the parameters can be applied in multiple ways
• ggtitle('yor title') is the same as labs(title='your title')
• See ?labs for its equivalent calls
• Many of the functions are siblings of a more general function
• geom_vline is the sibling of geom_abline
• theme_bw is a special version of theme
  • The default is theme_grey

head(WorldPhones)
str(WorldPhones)

##      N.Amer Europe Asia S.Amer Oceania Africa Mid.Amer
## 1951  45939  21574 2876   1815    1646     89      555
## 1956  60423  29990 4708   2568    2366   1411      733
## 1957  64721  32510 5230   2695    2526   1546      773
## 1958  68484  35218 6662   2845    2691   1663      836
## 1959  71799  37598 6856   3000    2868   1769      911
## 1960  76036  40341 8220   3145    3054   1905     1008
##  num [1:7, 1:7] 45939 60423 64721 68484 71799 ...
##  - attr(*, "dimnames")=List of 2
##   ..$ : chr [1:7] "1951" "1956" "1957" "1958" ...
##   ..$ : chr [1:7] "N.Amer" "Europe" "Asia" "S.Amer" ...

ggplot(WorldPhones, aes(x=rownames(WorldPhones), y=Asia)) + geom_line()
## Error: ggplot2 doesn't know how to deal with data of class matrix

• Remember: ggplot eat only data.frames

WorldPhones.DF <- as.data.frame(WorldPhones)
WorldPhones.DF$year <- rownames(WorldPhones.DF)
class(WorldPhones.DF) # this time we should be fine!

## [1] "data.frame"

ggplot(WorldPhones.DF, aes(x=year, y=Asia)) + geom_line(size=1.5)

ggplot(WorldPhones.DF, aes(x=as.numeric(year), y=Asia)) + geom_line(size=1.5)

Remember? Categorical x at default will not show null data.

• Not straightforward, usually need preprocessing
• Only accept long format

##      N.Amer Europe Asia year
## 1951  45939  21574 2876 1951
## 1956  60423  29990 4708 1956
## 1957  64721  32510 5230 1957
## 1958  68484  35218 6662 1958
## 1959  71799  37598 6856 1959
## 1960  76036  40341 8220 1960
## 1961  79831  43173 9053 1961

##   Value Region Year
## 1 45939 N.Amer 1951
## 2 60423 N.Amer 1956
## 3 64721 N.Amer 1957
## 4 68484 N.Amer 1958
## 5 71799 N.Amer 1959
## 6 76036 N.Amer 1960
## 7 79831 N.Amer 1961
## 8 21574 Europe 1951

WP.long$Year <- as.integer(as.character(WP.long$Year))
ggplot(WP.long, aes(x=Year, y=Value, color=Region)) + geom_line(size=1.5)

ggplot(WP.long,aes(x=factor(Year), y=Value))+
  geom_line(aes(linetype=Region,group=Region),size=1.5)

ggplot(WP.long, aes(x=Year, y=Value, linetype=Region)) + geom_line(size=1.5) +
  guides(linetype=guide_legend(reverse=TRUE))

load("rdata/for_exercise.RData")    #load sample data named dat
dat$date = as.Date(substr(dat$T.Max.Time, 1, 10))   # generate date
dat$city = factor(dat$city) # set city as actor
ggplot(dat,aes(x=date,y=RH))+
  geom_line(aes(group=city,colour=city),size=1.5)+
  # adjust the angle of date label  
  theme(axis.text.x = element_text(angle = 90, hjust = 1,vjust=0.5),
        text=element_text(size=20))

movies1 <- movies[!is.na(movies$budget),]
ggplot(movies1, aes(x=budget, y=rating)) + geom_point()

ggplot(movies1, aes(x=budget, y=rating)) + geom_point(shape=5, size=3)

This usually happens accidentally.

ggplot(movies1, aes(x=budget, y=rating, color=Action)) + geom_point()

ggplot(movies1, aes(x=budget, y=rating, color=factor(Action))) + 
    geom_point() + labs(color='Action Movie?')

ggplot(movies1, aes(x=budget, y=rating, color=factor(Action), shape=(length > 120))) + 
    geom_point(size=3) + labs(color='Action Movie?')

ggplot(movies, aes(x=votes, y=rating)) + geom_point() +
    stat_smooth(method=lm, level=.95) # add se=FALSE to disable CI

ggplot(movies, aes(x=votes, y=rating)) + geom_point() + stat_smooth()

ggplot(movies1, aes(x=budget, y=rating, color=factor(Action))) + 
    geom_point() + labs(color='Action Movie?') + stat_smooth(method=lm, se=FALSE)

lm_model <- lm(rating ~ budget, data=movies1)
ggplot(movies1, aes(x=budget, y=rating)) + geom_point() +
    geom_line(aes(x=budget, y=lm_model$fitted.values), color='blue')

starmovies <- movies[movies$votes > mean(movies$votes),]
starmovies <- starmovies[order(-starmovies$rating),][1:10,]
ggplot(starmovies, aes(x=votes, y=rating)) + geom_point() + geom_text(aes(label=title))

ggplot(starmovies, aes(x=votes, y=rating)) + geom_point(color='red') + 
    geom_text(aes(label=title), hjust=0, vjust=0, angle=20) +
    xlim(0, max(starmovies$votes)*2) +
    ylim(min(starmovies$rating), 9.2)

We only choose the movies with single type to simplify the question.

movietype <- colnames(movies)[18:24]
movies1_singletype <- movies1[rowSums(movies1[, movietype]) == 1,] # remove multi-typed
movietype_alt <- c(movietype[length(movietype)], movietype[-length(movietype)]) 
# convert multiple dummies into one factor as grouping var
# a little matrix operation will do the trick
dummies <- as.matrix(movies1_singletype[, movietype_alt])
movies1_singletype$Type <- factor(dummies %*% (1:length(movietype_alt)), labels=movietype_alt)

# Compute the Average Budget of Each Type
tapply(movies1_singletype$budget, movies1_singletype$Type, mean)

##       Short      Action   Animation      Comedy       Drama Documentary 
##    396133.1  32698189.6  32311451.6  11921970.6  10456690.5    729704.8 
##     Romance 
##   5603688.0

The first factor level of movietype, Short, is represented as the intercept term.

lmfit <- lm(as.formula("budget ~ Type"), movies1_singletype)
summary(lmfit)$coef

##                   Estimate Std. Error    t value     Pr(>|t|)
## (Intercept)       396133.1    1715935  0.2308556 8.174455e-01
## TypeAction      32302056.5    2062063 15.6649187 7.005190e-53
## TypeAnimation   31915318.5    4157723  7.6761525 2.316874e-14
## TypeComedy      11525837.5    1888686  6.1025705 1.202481e-09
## TypeDrama       10060557.4    1820528  5.5261750 3.604075e-08
## TypeDocumentary   333571.7    2881175  0.1157763 9.078389e-01
## TypeRomance      5207554.9    3713295  1.4024082 1.609149e-01

The last predictor, Short is combined into the intercept term.

# mean(movies1_singletype[movies1_singletype$Animation == 1, 'budget'])
lmfit <- lm(as.formula(paste('budget ~', paste(movietype, collapse=' + '))), 
            movies1_singletype)
summary(lmfit)$coef

##               Estimate Std. Error    t value     Pr(>|t|)
## (Intercept)   396133.1    1715935  0.2308556 8.174455e-01
## Action      32302056.5    2062063 15.6649187 7.005190e-53
## Animation   31915318.5    4157723  7.6761525 2.316874e-14
## Comedy      11525837.5    1888686  6.1025705 1.202481e-09
## Drama       10060557.4    1820528  5.5261750 3.604075e-08
## Documentary   333571.7    2881175  0.1157763 9.078389e-01
## Romance      5207554.9    3713295  1.4024082 1.609149e-01

What is the association between cost and rating, conditional on type?

movies1_reg_plot <- ggplot(movies1_singletype, aes(x=budget, y=rating, color=Type)) + 
  geom_point(shape=1) +
  
  # set fullrange=T will extend the fitted line outside the sample range
  stat_smooth(method=lm, se=FALSE, fullrange=FALSE, size=1.5) +
  
  # color is the grouping interface, hence scale_color_*
  scale_color_discrete(name='Movie Type: # of samples', 
                       labels=paste(levels(movies1_singletype$Type), ': ', 
                                    table(movies1_singletype$Type)))

movies1_reg_plot

Draw a scatter plot of Temprature and RH in Taipei, Taichung, Kaoshiung and Hualien in 2013-05, 2014-05 and 2015-05. Furthermore, each location has its own color and regression line.

Hint: Use following code to read all data and slice the data you want.

load("rdata/for_exercise.RData")    # load sample data names dat
dat$month <- substr(dat$T.Max.Time, 6, 7)
dat <- dat[dat$month == "05",]

ggplot(dat, aes(x=RH, y=Temperature, color=city)) + 
    geom_point(shape=1) + 
    stat_smooth(method = lm, size = 1.5)

interact_terms <- paste(paste(movietype, '*budget', sep=''), collapse=' + ')
lmfit <- lm(as.formula(paste('rating ~', interact_terms)), movies1_singletype)
tail(summary(lmfit)$coef)

##                         Estimate   Std. Error     t value  Pr(>|t|)
## Action:budget       1.580218e-08 3.331784e-08  0.47428594 0.6353367
## budget:Animation    8.246542e-09 3.379426e-08  0.24402199 0.8072334
## budget:Comedy      -6.222262e-10 3.337127e-08 -0.01864557 0.9851253
## budget:Drama        1.295424e-08 3.333295e-08  0.38863176 0.6975810
## budget:Documentary -8.504501e-08 9.010076e-08 -0.94388788 0.3453164
## budget:Romance     -3.983138e-08 4.086954e-08 -0.97459824 0.3298521

• None of the interactive term is statistically significant, indeed
• Visualization != Analysis (Our eyes were not born to work on numbers.)
• Plots can be easily manipluated to be misleading, accidentally or on purpose

from: http://sape.inf.usi.ch/quick-reference/ggplot2

ggplot(data=humidity)+geom_line(aes(x=date,y=humidity))

##         date location humidity
## 1 2015-05-01    \xaaὬ       91
## 2 2015-05-02    \xaaὬ       86
## 3 2015-05-03    \xaaὬ       82
## 4 2015-05-04    \xaaὬ       78
## 5 2015-05-05    \xaaὬ       90
## 6 2015-05-06    \xaaὬ       83

##         date humidity
## 1 2015-05-01       91
## 2 2015-05-02       86
## 3 2015-05-03       82
## 4 2015-05-04       78
## 5 2015-05-05       90
## 6 2015-05-06       83

 ggplot(pressure,aes(x=temperature,y=pressure))+
  geom_point()+
  stat_smooth()

## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.

hum=filter(humidity,rainfall>0) %>% select(location,rainfall) %>% 
  group_by(location) %>% summarise(ave.rain=mean(rainfall))
ggplot(hum,aes(x=location,y=ave.rain))+geom_bar(aes(fill=location),stat='identity')

ggplot(hum,aes(x=location,y=ave.rain))+
  geom_bar(aes(fill=location),stat='identity')+
  coord_polar(theta='y')

ggplot(hum,aes(x="",y=ave.rain))+
  geom_bar(aes(fill=location),stat='identity')+
  coord_polar(theta='y')

ggplot(humidity,aes(x=date,y=humidity))+
  geom_line(aes(group=location,colour=location),size=1.5)+
  scale_x_date(breaks = "7 days",labels = date_format("%m/%d"))+
  facet_wrap(~location)

• 控制圖中的元素與主題

 ggplot(humidity,aes(x=date,y=humidity))+
   geom_line(aes(group=location,colour=location),size=1.5)+
  theme_light(base_size = 20)

• ggplot2 Official document
• R Graphics Cookbook
• Introduction to Programming R
  • Knowlege of general programming is requisite

plot(movies1$budget, movies1$rating) # base solution
abline(h=median(movies1$rating), col='red')
text(x=max(movies1$budget)*.9, y=median(movies1$rating), 
     labels='Median of Rating', col='red', pos=1)

brggp <- ggplot(movies1, aes(x=budget, y=rating)) + geom_point() 
brggp + geom_hline(yintercept=median(movies1$rating)) # ?geom_abline for general setup
# brggp + geom_hline(data=movies1, aes(yintercept=median(rating)))  # the same
# brggp + geom_hline(aes(yintercept=median(movies1$rating)))        # the same

brggp + geom_hline(yintercept=median(movies1$rating), color='red') + 
  annotate('text', x=Inf, y=median(movies1$rating), 
           label='Medaion of Rating', color='red', vjust=1.2, hjust=1)
# Don't use geom_text for single annotation to avoid overplotting

shaw <- movies1[grep('Shawshank Redemption', movies1$title, fixed=TRUE),]
brggp + annotate('segment', xend=shaw$budget, yend=shaw$rating, x=Inf, y=-Inf,
                 arrow=grid::arrow(), color='red') +
  annotate('text', label='The Shawshank Redemption?', x=Inf, y=-Inf,
           hjust=1.5, vjust=-1, color='red')

yearcount <- aggregate(title ~ year, data=movies, FUN=length)
ggplot(yearcount, aes(x=year, y=title)) + geom_line() +
  annotate('rect', xmin=1990, xmax=2000, ymin=-Inf, ymax=Inf, fill='blue', alpha=.25)

gg <- ggplot(movies1_singletype, aes(x=rating, y=..density..)) + geom_bar()
gg + facet_grid(Action ~ .) # Plot with grouping variable in different window (Vertical)

# Plot with grouping variable in different window (Horizontal)
gg + facet_grid(. ~ Action) 

movies1_singletype$modern <- (movies1_singletype$year > 2000)
ggplot(movies1_singletype, aes(x=rating, y=..density..)) + 
  geom_bar() + facet_grid(modern ~ Action)

movies1_singletype$rated <- (movies1_singletype$mpaa != '')
ggplot(movies1_singletype, aes(x=rating, color=modern)) + 
  geom_line(stat="density") + facet_grid(Type ~ rated)

• Way 1: Change the grouping var in data.frame to reflect the alternate labels
• Way 2: Write customized labeller function for facet_grid

print(label_value) # the default labeller plugg in facet_grid(..., labeller)

## function (variable, value) 
## as.character(value)
## <environment: namespace:ggplot2>

mylabeller <- function(variable, value){
  if ( variable=='rated' ) 
    value <- ifelse(value == TRUE, 'Rated Movies', 'Unrated Movies')
  else if ( variable=='Type' ) 
    as.character(value)
}

ggplot(movies1_singletype, aes(x=rating, color=modern)) +
  geom_line(stat="density") + facet_grid(Type ~ rated, labeller=mylabeller)

library(gridExtra)
drawPoint <- function(i) {
  ggplot(data.frame(x=1, y=1), aes(x=x,y=y)) + 
    geom_point(shape=i, size=5) +
    ggtitle(sprintf('shape=%s',i)) + 
    theme(axis.text.x=element_blank(), axis.text.y=element_blank()) +
    xlab(NULL) + ylab(NULL)
  }
drawPoint(25)

symbol_points <- mapply(drawPoint, 1:25, SIMPLIFY=FALSE)
symbols <- do.call(arrangeGrob, symbol_points)
# plot(symbols)